This invention relates to the art of image processing, with electronic circuits for edge extraction/enhancement using the Sobel algorithm to improve the picture quality.
Rapid advances have been made during the past several years in large-scale integrated circuit technology. These advances have had a significant impact on many signal processing functions for advanced reconnaissance and weapon delivery systems. These systems use photographic or video images to survey an area to detect enemy vehicles/targets, identify them and cue them as to priority as a strike objective (for example: a tank vs. a jeep or a missile site vs. a truck).
One approach taken to solve this detection and identification problem is to first perform an edge extraction/enhancement on a video or video equivalent (i.e., infrared, forward looking infrared, laser scan, etc.) signal. This involves scanning the image to form pixels, and then converting the pixel values to digital form, commonly with an eight-bit data word for each pixel. The edges in these images can be used in a number of various ways. They can be used for pattern matching or fed into a subsystem for further processing.
However, any subsequent subsystem is dependent on the quality of the edges found. There are a number of algorithms for analyzing images from photographs or video frames, in which individual pixels are first converted to digital form. Many of these algorithms use a 3.times.3 window of pixels in each step of processing. Investigations have shown that the Sobel square root algorithm is the best 3.times.3 window algorithm studied to date.
While this and other algorithms can be executed easily at low data rates using general purpose minicomputers or even commercial microprocessors, it is usually not possible to execute them in real time in an airborne environment because of excessive size, weight, power dissipation, and cost. The key to effective system design is to apply large scale integrated circuit technology (LSIC) to minimize the overall component count and variety of components while absorbing as much as possible of the control and timing logic onto the information processing chips themselves. However, the complexity of the Sobel square root algorithm has precluded its use in real-time or near real-time systems.
A simplified form is the Sobel magnitude algorithm, (Equation (1) which is ##EQU1## where
______________________________________ a b c h z d represents the 3 .times. 3 window. g f e ______________________________________
In one case analog charge coupled devices (CCD's) were used with the above algorithm that operated at 4 MHZ but yielded only a four-bit output. In another a number of 68000 microprocessors are used in parallel to achieve near real-time speeds.
A large number of image processing algorithms have been evaluated. This work was mainly looking into bandwidth reduction, edge enhancement and edge detection, with recent effort concentrating on edge detection. The purpose of these studies was to determine which of the algorithms could be implemented on a single monolithic integrated circuit. Though a single chip was the major goal, the quality and accuracy of the algorithm was also a major factor in the determination of its applicability. The Sobel square root algorithm most nearly met both of the above criteria, this algorithm (Equation 2) is EQU S={[(a+2b+c)-(g+2f+e)].sup.2 +[(a+2h+g)-(c+2d+e)].sup.2 }.sup.1/2( 2)
In order to solve the Sobel square root algorithm one usually first solves the absolute magnitude portion of the Sobel magnitude equation. Before summing the absolute values each value is squared. The summation is then followed by a square root operation. These additional steps increase the hardware complexity of the Sobel tremendously, however, it provides the best linear response between actual and detected edge orientation.